Manufacture modeling and monitoring

ABSTRACT

Methods, apparatus, and computer program products for analyzing, monitoring, and/or modeling the manufacture of a type of part by a manufacturing process. Non-destructive evaluation data and/or quality related data collected from manufactured parts of the type of part may be aligned to a simulated model associated with the type of part. Based on the aligned data, the manufacturing process may be monitored to determine whether the manufacturing process is operating properly; aspects of the manufacturing process may be spatially correlated to the aligned data; and/or the manufacturing process may be analyzed.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. application Ser. No.14/211,600 filed on Mar. 14, 2014 by Joseph M. Kesler et al., and thatApplication claims the benefit of U.S. Provisional Application No.61/791,139 filed on Mar. 15, 2013 by Joseph M. Kesler et al., the entiredisclosure of those Applications being incorporated by reference hereinin their entireties.

FIELD OF THE INVENTION

The present invention relates to computing systems, and moreparticularly to the modeling and monitoring of part manufacture withinspection data and/or non-destructive evaluation (“NDE”) data.

BACKGROUND OF THE INVENTION

Non-destructive Evaluation and Inspection (“NDE/I”) technologiesgenerally provide ways to nondestructively scan, image, sense orotherwise evaluate characteristics of materials and/or components. Inparticular, NDE/I technologies may be used to detect minute flaws anddefects in those materials and/or component parts. As such, NDE/Itechnologies have become increasingly used to help assure structural andfunctional integrity, safety, and cost effective sustainment of variousassets, during both initial manufacture and operational service.

Non-destructive evaluation (“NDE”) data is often based on raw datagathered from NDE data collection devices and may include x-ray imagesof at least a portion of a part or asset, such as the wing of anaircraft or some other type of part that may be manufactured. NDE datais often large in size, associated with merely a portion of the part,and also must be matched with a particular location on the part. Suchlarge data sets of NDE data become increasingly difficult to manage,particularly if such NDE datasets are collected for many partsmanufactured in a manufacturing process. In addition, other types ofquality related data, including for example visual inspection data froman inspector, may further complicate management and analysis of NDE dataand/or quality related data on a large scale, such as in a manufacturingenvironment.

To determine wear and tear, structural damage and/or otherirregularities of a part may require the analysis of tens (if nothundreds) of individual datasets of NDE data and/or quality relateddata. This may result in numerous datasets of NDE data and/or qualityrelated data for each manufactured part of a manufacturing process, andthus even more datasets of NDE data and/or quality related data for aplurality of parts manufactured by the manufacturing process. As eachdataset is analyzed, this results in large amounts of data that aredifficult to categorize and otherwise analyze in whole. Moreover, theNDE data and/or other such quality related data may be discarded afterit has been analyzed, and thus there is often little inspection datarelated to the manufacture of parts over time.

To account for such data management issues, in some conventionalsystems, NDE data and/or quality related data may be discarded orignored if such data does not correspond to a part on which amanufacturing defect has been detected. Moreover, in conventionalsystems, analysis of NDE data and/or quality related data is timeconsuming due to the cumbersome nature of the data. Hence, whenutilizing NDE data and/or other such types of inspection data for partsmanufactured in a manufacturing process, the usefulness of such NDE dataand/or other such types of inspection data is limited due to theinefficiencies associated with management and analysis of such data.

Consequently, there is a continuing need to manage and analyzeinspection data for a manufacturing process.

SUMMARY OF THE INVENTION

Embodiments of the invention provide for a method, apparatus, andprogram product to manage and analyze non-destructive evaluation (“NDE”)data and/or other types of quality related data corresponding to partsmanufactured by a manufacturing process to thereby monitor and model themanufacturing process.

Consistent with embodiments of the invention, a manufacture of a type ofpart may be monitored. In these embodiments, an NDE dataset associatedwith a particular part of the type of part may be received, where eachNDE dataset for the part includes NDE data, where such NDE data may bereferred to herein as one or more NDE data points, and each NDE datasetmay correspond to data (i.e., raw data) collected during non-destructiveevaluation of the particular part. The NDE dataset may be aligned to asimulated model associated with the type of part, where such aligningmay include aligning NDE data points of the dataset to correspondingsimulated locations on the simulated model. Respective NDE data pointsof the aligned NDE data points may be analyzed to determine a spatiallycorrelated statistic corresponding to the particular part based at leastin part on the respective NDE data points and the correspondingsimulated locations of the respective NDE data points for the particularpart. The spatially correlated statistic may be determined for a groupof proximate (i.e., proximately aligned on the simulated model) NDE datapoints, where the spatially correlated statistic may be based at leastin part on a measurement value of each NDE data point. Output data maybe generated based at least in part on the spatially correlatedstatistic.

These and other advantages will be apparent in light of the followingfigures and detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention and,together with a general description of the invention given above and thedetailed description of the embodiments given below, serve to explainthe principles of the invention.

FIG. 1 is a diagrammatic illustration of a computing system, userdevice, and NDE/I collection devices configured to collect and analyzenon-destructive evaluation (“NDE”) data consistent with embodiments ofthe invention to analyze, model, and/or monitor a manufacturing process.

FIG. 2 is a block diagram of that illustrates data components ofmanufacturing data that may be generated and/or processed by thecomputing system and/or user device of FIG. 1 to analyze, model, and/ormonitor a manufacturing process.

FIG. 3 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 tomonitor the manufacturing process.

FIG. 4 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 duringthe monitoring of the manufacturing process illustrated in FIG. 3.

FIG. 5 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 duringthe monitoring of the manufacturing process illustrated in FIG. 3.

FIG. 6 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 duringthe monitoring of the manufacturing process illustrated in FIG. 3.

FIG. 7 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 duringthe monitoring of the manufacturing process illustrated in FIG. 3.

FIG. 8 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 duringthe monitoring of the manufacturing process illustrated in FIG. 3.

FIG. 9 is a flowchart that illustrates a sequence of operations that maybe performed by the computing system and/or user device of FIG. 1 duringthe monitoring of the manufacturing process illustrated in FIG. 3.

FIG. 10 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.3.

FIG. 11 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.3.

FIG. 12 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.3.

FIG. 13 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomonitor the manufacture of a type of part by a manufacturing process.

FIG. 14 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.13.

FIG. 15 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.13.

FIG. 16 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.13.

FIG. 17 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomonitor the manufacture of a type of part by a manufacturing process.

FIG. 18 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomodel the manufacture of a type of part by a manufacturing process.

FIG. 19 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 toanalyze a part manufactured by a manufacturing process.

FIG. 20 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomonitor the manufacture of a type of composite aircraft of part by amanufacturing process.

FIG. 21 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 toanalyze manufacture of a type of part by a manufacturing process.

FIG. 22 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 toanalyze manufacture of a type of part by a manufacturing process.

FIG. 23 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the analysis of the manufacturing process illustrated in FIG. 22.

FIG. 24 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the analysis of the manufacturing process illustrated in FIG. 22.

FIG. 25 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the analysis of the manufacturing process illustrated in FIG. 22.

FIG. 26 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 toanalyze manufacture of a type of part by a manufacturing process.

FIG. 27 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the analysis of the manufacturing process illustrated in FIG. 26.

FIG. 28 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the analysis of the manufacturing process illustrated in FIG. 26.

FIG. 29 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomodel the manufacture of a type of part by a manufacturing process.

FIG. 30 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the modeling of the manufacturing process illustrated in FIG. 29.

FIG. 31 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the modeling of the manufacturing process illustrated in FIG. 29.

FIG. 32 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomonitor the manufacture of a type of part by a manufacturing process.

FIG. 33 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1 tomonitor the manufacture of a type of part by a manufacturing process.

FIG. 34 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.33.

FIG. 35 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.33.

FIG. 36 is a flowchart that illustrates a sequence of operations thatmay be performed by the computing system and/or user device of FIG. 1during the monitoring of the manufacturing process illustrated in FIG.33.

FIG. 37 is a diagrammatic illustration of an example graphical userinterface that includes a display representation of a simulated model ofa type of part that may be output on a display associated with the userdevice and/or computing system of FIG. 1.

FIG. 38 is a diagrammatic illustration of the example graphical userinterface of FIG. 37 where the display representation of the simulatedmodel includes a visual representation of aligned NDE data on thesimulated model.

FIGS. 39A-C are diagrammatic illustrations of the example graphical userinterface of FIG. 38 where the display representation of the simulatedmodel includes a visual representation of aligned indications ofpotential problems on the simulated model, and FIG. 39B is an enlargedview of a portion of FIG. 39A.

FIG. 40 is a diagrammatic illustration of the example graphical userinterface of FIG. 37 where the display representation of the simulatedmodel includes a visual representation of aligned indications ofpotential problems and a highlighted area selected by user input thatindicates an area of interest.

FIGS. 41A-B are diagrammatic illustrations of an example graphicalinterface that includes a display representation of a simulated model ofa type of part including a visual representation of indications ofpotential problems associated with a first part of the type of part thatmay be output on a display associated with the user device and/orcomputing system of FIG. 1.

FIGS. 42A-B are diagrammatic illustrations of the example graphicalinterface of FIGS. 41A-B, where the display representation of thesimulated model includes a visual representation of indications ofpotential problems associated with a second part of the type of part.

FIG. 43 is an example control chart for a manufacturing process that maybe generated by the computing system and/or user device based onmanufacturing data, NDE data, and/or quality related data associatedwith the manufacturing process.

It should be understood that the appended drawings are not necessarilyto scale, presenting a somewhat simplified representation of variouspreferred features illustrative of the basic principles of theinvention. The specific design features of the sequence of operations asdisclosed herein, including, for example, specific dimensions,orientations, locations, and shapes of various illustrated components,will be determined in part by the particular intended application anduse environment. Certain features of the illustrated embodiments mayhave been enlarged or distorted relative to others to facilitatevisualization and clear understanding.

DETAILED DESCRIPTION

Embodiments of the invention provide for a method, apparatus, andprogram product to model and/or monitor a manufacturing process usingNDE data and/or quality related data collected from parts manufacturedby the manufacturing process. Furthermore, embodiments of the inventionorganize and align such data by aligning the data to a simulated modelof a type of part associated with the manufactured parts. In someembodiments, NDE data that corresponds to raw data collected by one ormore NDE/I devices during non-destructive evaluation of one or more ofthe manufactured parts. In some embodiments, other types of qualityrelated data may be utilized. For example, quality related data maycomprise indication data collected during inspection by one or morepersonnel tasked with inspecting parts manufactured in the manufacturingprocess (e.g., quality control engineers/technicians). The indicationdata may comprise indications of potential problems at locations onparts of the type of part. For example, such quality related data mayinclude visually detected defects indicated on non-compliance reportsgenerated during inspection of one or more of the manufactured parts.

In general, some embodiments of the invention may be described withrespect to NDE datasets; however, the invention is not so limited.Quality related data, not necessarily corresponding to raw datacollected by NDE/I devices may be utilized consistent with someembodiments of the invention. For example, some embodiments of theinvention may analyze and/or manage information derived fromnon-compliance reports corresponding to a manufacturing process. Thesenon-compliance reports may comprise indication data that includes one ormore indications of one or more visually detected defects on parts of atype of part manufactured by the manufacturing process. In general, suchnon-compliance reports may be generated by a quality inspector trainedto inspect parts manufactured by the manufacturing process. Moreover,other types of relevant quality related data may be included in anon-compliance report in addition to or in place of indications ofvisually detected defects depending on the type of part and themanufacturing process. As another example, defects/indications may bedetected via ultrasonic scanning/testing and may be included in anon-compliance report and/or input directly to a simulated model viauser input, where an operator may manually enter suchdefects/indications.

In general, embodiments of the invention align one or more NDE datasetscomprising NDE data points and/or one or more quality related datasetscomprising quality related data points (i.e., indications of potentialproblems) to a simulated model associated with a type of manufacturedpart. For example, a portion of a type of part may be represented by thesimulated model, and NDE data points collected during non-destructiveevaluation of a manufactured part of the type of part may be aligned tocorresponding simulated locations on the simulated model. Therefore,aligning the NDE dataset and/or quality related dataset to the simulatedmodel comprises aligning at least one data point of the dataset to acorresponding location on the simulated model. In general, at least asubset of data points of the dataset may be aligned to a correspondinglocation on the simulated model.

According to embodiments of the invention, NDE data and/or qualityrelated data may be aligned to a simulated model. Methods and apparatusfor aligning NDE data and/or quality related data to a simulated modelis described in further detail in U.S. Pat. No. 8,108,168 to Sharp etal., entitled “MANAGING NON-DESTRUCTIVE EVALUATION DATA,” filed Mar. 12,2009, which is incorporated by reference herein in its entirety.

In some embodiments, the NDE data and/or quality related data may beassociated with inspection information. The inspection information mayassociate the NDE data and/or quality related data with particularinformation that may be useful to align the NDE data, indicate potentialproblems, and/or otherwise provide data about the type of part. In someembodiments, the inspection information may include data associated witha location of a particular part to which the associated datacorresponds, an identification of the particular part, a history of theparticular part, a time at which the NDE data was captured, a date atwhich the NDE data was captured, an identification of an NDE sessionassociated with the NDE data, an annotation associated with the NDE data(e.g., such as an annotation that includes an indication of a potentialproblem), an identification of an inspector associated with the NDEdata, an identification of a series of NDE data in which the NDE datawas captured, an identification of the location of the NDE data in theseries of NDE data, an orientation associated with the NDE data, aunique identification of the NDE data, an identification of the modalityof NDE data collection device used to capture the NDE data, and/orcombinations thereof. The inspection information may be determinedautomatically, and/or captured by a computer, during, or after thecapture of the NDE data.

In some embodiments, inspection information may include at least oneindication of a potential problem and a location thereof on the NDEdata, such that the indication may be aligned to a correspondingsimulated location on the simulated model. In some embodiments, the atleast one indication aligned to the simulated model may be included in adisplay representation associated with the type of part and based on thesimulated model. For example, the display representation may comprise athree dimensional representation of the type of part that may be outputto a computer display or other such viewing device. In this example, anindication of the potential problem associated with the inspectioninformation may be a visual indicator located at the correspondinglocation on the three-dimensional representation.

In some embodiments, a plurality of datasets of NDE data (e.g., aplurality of individual instances of NDE data), at least some of whichmay be associated with inspection information, may be aligned to thesimulated model. As such, indications in turn associated with theinspection information of the plurality of datasets may be viewed fortrends of indications, where such trends may correspond to manufacturingtrends associated with the manufacture of the type of part by themanufacturing process.

Based on aligned NDE data some embodiments of the invention may monitora manufacturing process. In these embodiments, a dataset of NDE data maybe received for each of a plurality of manufactured parts of a type ofpart manufactured by the manufacturing process. Embodiments of theinvention may align the received data for each manufactured part to thesimulated model. A spatially correlated statistic may be determined foreach part based on the aligned NDE data, and a manufacturing trend maybe determined based on the spatially correlated statistics and monitoredfor the manufacturing process.

A spatially correlated statistic may generally correspond to a valueassociated with an area, region, volume, and/or other such spatiallyrelated feature of the type of part. In general, the spatiallycorrelated statistic may define a value for such spatially relatedfeature that is based at least in part on NDE data and/or qualityrelated data collected for the spatially related feature. For example,each part of a type of part may include a particular portion for whichNDE data collected for the part indicates a measured value of theporosity at a plurality of locations corresponding to the particularportion. Embodiments of the invention may determine an average porosityfor the particular portion of each part based on the NDE data collectedfor each part at the plurality of locations. Other types of spatiallycorrelated statistics may be determined depending on the type of NDEdata collected and/or the type of part, including for example, averagethickness, average distance between specified features, averageamplitude, average quantity of indications of potential problems,density of indications of potential problems, a standard deviation ofany of the previously mentioned values, and/or other such types ofstatistical data that may be determined based on the types of collectedNDE data.

For example, based on the spatially correlated statistics, themanufacturing trend may indicate that while the manufacturing process ispresently producing acceptable parts, the manufacturing trend indicatesthat the manufacturing process will begin producing unacceptable partsin the future. Hence, based on the spatially correlated statistics,embodiments of the invention may determine whether the manufacturingprocess is operating properly, and if the manufacturing trend indicatesthat a problem is likely to develop, actions may be taken prior to themanufacturing process possibly manufacturing unacceptable parts.

In some embodiments, a manufacturing process may be modeled based atleast in part on NDE data collected for one or more parts of a type ofpart manufactured by the manufacturing process. In these embodiments, atleast one NDE dataset may be received, where each NDE dataset comprisesNDE data points of NDE data that corresponds to data collected duringnon-destructive evaluation of the a respective part of the type of part.The NDE data points may be aligned to corresponding simulated locationson a simulated model associated with the type of part. In theseembodiments, the NDE data may include associated inspection informationthat indicates one or more potential problems detected on the particularpart. In addition, manufacturing data may be associated with thesimulated model, where the manufacturing data may indicate variousinformation associated with the manufacturing process and one or morecorresponding simulated locations on the simulated model. For example,the manufacturing data may indicate a manufacturing step of themanufacturing process associated with one or more correspondingsimulated locations on the simulated model. In this example, if amanufacturing step of the manufacturing process involved applying anadhesive to a particular location on each manufactured part, themanufacturing data may indicate at a corresponding simulated location onthe simulated model the adhesive application step. Hence, in thisexample, if a potential problem were indicated at a correspondingsimulated location associated with the adhesive application step asindicated in the manufacturing data, the modeling of the manufacturingprocess may indicate that a problem is potentially occurring in theadhesive application step.

Therefore, as illustrated by this example, NDE data and/or inspectioninformation may be organized spatially on the simulated model, andmanufacturing data may also be organized spatially on the simulatedmodel, and as a result, the manufacturing process may be modeled on thespatially simulated model such that NDE data or other such data may becorrelated to aspects of the manufacturing process. The manufacturingdata may include for example, data that indicates a manufacturing stepof the manufacturing process, data that indicates a manufacturingapparatus utilized in the manufacturing process, data indicating amanufacturing tool utilized in the manufacturing process, dataindicating a process parameter of the manufacturing process, dataindicating evaluation equipment utilized in collecting raw datacorresponding to the NDE data for parts manufactured by themanufacturing process, and/or other such types of information related tothe manufacturing process.

Hardware and Software Environment

Turning to the drawings, wherein like numbers denote like partsthroughout the several views, FIG. 1 illustrates a hardware and softwareenvironment for one or more computing systems 10, one or more userdevices 12 and one or more NDE/I collection devices 14 consistent withsome embodiments of the invention. In general, embodiments of theinvention may be described in the context of a single computing system10 and/or user device 12, but as shown in FIG. 1, the invention is notso limited. In particular, embodiments of the invention may beimplemented in distributed processing systems, including for example, aplurality of interconnected computing systems 10 and/or user devices 12that are configured to perform operations consistent with embodiments ofthe invention in a distributed manner (i.e., across a plurality ofdistributed processors using data stored to and read from a plurality ofdistributed memory locations on a plurality of memory devices).

In general, the NDE/I collection devices 14 may comprise devicesconfigured to collect non-destructive evaluation/inspection data. SuchNDE/I collection devices may comprise one or more cameras (e.g., tocapture still images for visualization, videos for visualization, and/orfor sherography, etc.), thermograpic cameras (e.g., to capture athermographic image), borescopes, fiberscopes, x-ray machines (e.g., tocapture still images, to use with computed radiography, to use withdirect and/or digital radiography, etc.), ultrasound machines, CTscanners, MRI machines, eddy current detectors, liquid penetrantinspection systems, magnetic-particle inspection systems, coordinatemeasuring machines, and/or other such types of non-destructiveevaluation/inspection devices. As such, the types of NDE data includedin NDE datasets may vary, and embodiments of the invention may modeland/or monitor manufacture of a type of part by processing various typesof NDE data.

Computing system 10 and/or user device 12, for purposes of thisinvention, may represent any type of computer, computing system, server,disk array, or programmable device such as a multi-user computer,single-user computer, handheld device, networked device, mobile phone,gaming system, etc. Computing system 10 and/or user device 12 may beimplemented using one or more networked computers, e.g., in a cluster orother distributed computing system. Hence, it should be appreciated thatthe computing system 10 and/or user device 12 may also include othersuitable programmable electronic devices consistent with the invention

With reference to FIG. 1, as shown, the computing system 10 may compriseat least one processing unit (CPU′) 16 and memory 18. Each processor 16may be one or more microprocessors, micro-controllers, fieldprogrammable gate arrays, or ASICs, while memory 18 may include randomaccess memory (RAM), dynamic random access memory (DRAM), static randomaccess memory (SRAM), flash memory, and/or another digital storagemedium. As such, memory 14 may be considered to include memory storagephysically located elsewhere in computer 10, e.g., any cache memory inthe at least one CPU 16, as well as any storage capacity used as avirtual memory, e.g., as stored on a mass storage device, a computer, oranother controller in communication with the computing system. Inaddition, the computing system 10 may comprise a user interface 20,where the user interface 20 generally comprises one or more input/outputdevices for interfacing with a user, such as a display, a keyboard, amouse, speakers, a microphone, a video camera, a touch input baseddevice (e.g., a touchscreen), and/or other such devices. Furthermore,the computing system 10 may comprise a network interface 22, where thenetwork interface 22 is generally configured to communicate data over acommunication network 24. Network 24 generally comprises one or moreinterconnected communication networks, including for example, a cellularcommunication network, a local area network, a wide area network, publicnetworks (e.g., the Internet), an enterprise private network, high speeddata communication interconnects, and/or other such communicationnetworks.

The memory 18 stores at least one application 26 and/or an operatingsystem 28, where the application 26 and/or operating system generallycomprise program code in the form of instructions that may be executedby the processor 16 to cause the processor to perform one or moreoperations consistent with embodiments of the invention. For example,the application 26 and/or operating system 28 may include program codein the form of executable instructions that may cause the processor tomonitor and/or model a manufacturing process based on data received atthe computing system 10 and/or processor 16. It will be appreciated thatvarious applications, components, programs, objects, modules, etc. mayalso execute on one or more processors in another networked devicecoupled to computing system 10 via the network 24, e.g., in adistributed or client-server computing environment

In general, the memory 18 of the computing system 10 may store datautilized by embodiments of the invention. For example, the CPU 16 mayread from and/or write data to the memory 18 when performing one or moreoperations consistent with some embodiments of the invention. Asdiscussed above, the memory 18 may generally represent memory accessibleby the computing system 10, such as accessible databases connected overthe communication network 24 and/or other such data communicationnetworks. Furthermore, the memory 18 includes a database managementsystem in the form of a computer program that, when executing asinstructions on the processor 16, is used to read from and/or write toaccessible data structures (e.g., databases) of the memory. As shown inFIG. 1, the memory 18 may store a manufacturing database 30, that storesmanufacturing data 32 associated with a manufacturing process thatmanufactures a type of part. In addition, the memory 18 may store modeldata 34 associated with the type of part manufactured in themanufacturing process, NDE data 36 associated with the type of partand/or manufacturing process, quality related data 38 associated withthe type of part and/or manufacturing process, and/or inspectioninformation 40 associated with the type of part and/or manufacturingprocess.

While in FIG. 1, the manufacturing database 30, model data 34, NDE data,quality related data 36, and inspection information 40 are illustratedas separate data structures, the invention is not so limited. Thecomputing system 10 may comprise one or more data structures configuredas database structures storing the data described herein. Such one ormore databases may be configured in any database organization and/orstructure, including for example, relational databases, hierarchicaldatabases, network databases, and/or combinations thereof.

As shown in FIG. 1, each user device 12 generally comprises a processor(CPU′) 42 and a memory 44. In general, the user device 12 may be apersonal computer, laptop computer, hand-held computing device, tabletcomputer, and/or other such types of computing devices. As shown, theuser device 12 may comprise a user interface 46 configured to receiveinput data from a user and output data to a user via one or moreinput/output devices. Such input/output devices, include, for example akeyboard, mouse, display, touch screen, speakers, microphone, etc. Suchinput/output devices are generically represented by a human machineinterface (HMI′) 48 in FIG. 1. Furthermore, the user device 12 mayinclude a network interface 50, where, as described above with respectto the computing system 10, the network interface is configured totransmit data to and receive data from the communication network 24. Forexample, the computing system 10 and the user device 12 may communicatedata therebetween over the communication network 24 via the networkinterfaces 22, 50. Furthermore, the user device 12 may be under thecontrol of an operating system (‘OS’) 52 stored in the memory 44. Asdescribed previously, the operating system 52 and/or an application 54stored in the memory 44 may comprise program code in the form ofexecutable instructions, that, when executed by the processor 42 maycause the processor to perform or cause to be performed one or moreoperations consistent with embodiments of the invention.

FIG. 2 is a diagrammatic illustration of one embodiment of a pluralityof components of manufacturing data 32 consistent with embodiments ofthe invention. As will be described in further detail, herein themanufacturing data may generally comprise data associated with one ormore aspects of a manufacturing process and/or a type of part. Asmentioned previously, the manufacturing data 32 may be stored in amanufacturing database 30. As such, in some embodiments of theinvention, the data illustrated as a component of the manufacturing data32 may be stored relationally, such that the relationship(s) between thedifferent types of data of the manufacturing data 32 may be stored. Asshown, the manufacturing data 32 may store location data 60, wherelocation data 60 may identify one or more simulated locations on asimulated model of the type of part. In general, location data 60 may berelated to one or more other types of data to therebycorrelate/associate such data to one or more simulated locations on thesimulated model of the type of part.

In some embodiments, the manufacturing data 32 may store manufacturingstep data 62 that identifies one or more manufacturing steps associatedwith the manufacturing process and/or type of part. Similarly, themanufacturing data 32 may comprise manufacturing apparatus data 64 thatidentifies one or more manufacturing apparatuses associated with themanufacturing process and/or type of part. In general, a manufacturingapparatus may be equipment utilized in the manufacturing process (e.g.,cutting tools, molds, drilling tools, resin pumps, vacuum pumps,autoclaves, adhesive dispensers, carbon fiber tape rollup machines,carbon fiber placement machines, industrial ovens for curing, etc.) Themanufacturing data 32 may comprise manufacturing tool data 66 thatidentifies one or more manufacturing tools associated with themanufacturing process and/or the type of part. In general, amanufacturing tool may be a portion of equipment that isreplaceable/consumable and/or experience wear (e.g., drill bits, cuttingblades, mold seams, thermocouples, seals/gaskets, vacuum ports, resinflow paths, resin injection ports, mold planes, caul planes, mandrelsections, bladders, injection nozzles, etc.) The manufacturing data 32may comprise manufacturing parameter data 68 that identifies one or moremanufacturing parameters associated with the manufacturing processand/or the type of part. In general a manufacturing parameter and/ormanufacturing step parameter may be considered a parameter that mayaffect the manufacturing process (e.g., temperature in a curing oven,pressure in a mold, ratio for an adhesive mixture, pressure of a watercutting apparatus, age of material, temperature of material, viscosityof a resin, anomalies in material structure, etc.). Moreover, anadditional consideration with respect to the manufacturing parametersmay be the intended manufacturing parameter as compared to an actualmanufacturing parameter, where embodiments of the invention may analyze,model, and/or monitor a manufacturing process based on combinationsthereof. Furthermore, a manufacturing parameter may comprise anomaliesreported by the manufacturing equipment (e.g., a manufacturingapparatus, an NDE/I collection device, etc.), including for example,data stored in machine logs for manufacturing equipment used in themanufacturing process. These logs may indicate events (i.e., anomalies)that may affect the manufacture of parts by the manufacturing process.For example, if a manufacturing apparatus of a manufacturing process wasa fiber placement system, a machine log for such fiber placement systemmay include data related to loss of tension, fiber slippage, compactionpressure, deviations in velocity of fiber layup, and/or other suchevents/anomalies that may affect the manufacture of a part in themanufacturing process. The manufacturing parameter data may store datarelated to such anomalies for the various manufacturing equipmentutilized in the manufacturing process. The manufacturing data 32 maycomprise evaluation equipment data 70 that identifies one or moreevaluation equipment (i.e., NDE/I devices) 44 associated with themanufacturing process and/or the type of part. The manufacturing data 32may comprise possible root cause problem data 72 that identifies one ormore root cause problems associated with the manufacturing processand/or the type of part. The manufacturing data 32 may comprisemanufacturing defect data 74 that identifies one or more manufacturingdefects associated with the manufacturing process and/or type of part.In general, the one or more identified manufacturing defects may bebased on previous analysis of the manufacturing process (i.e.,historical data for previously manufactured parts). The manufacturingdata 32 may comprise spatially correlated statistic data 76 thatindicates one or more spatially correlated statistics associated withthe manufacturing process and/or type of part. The manufacturing data 32may comprise manufacturing trend data 78 that indicates one or moremanufacturing trends associated with the manufacturing process and/ortype of part. Furthermore, the manufacturing data 32 may compriseproblem indication data 80 that indicates one or more potential problemsthat may be associated with the type of part.

As discussed, the manufacturing data 32 may be organized relationallysuch that relationships between the types of data may be indicated. Forexample, location data 60 may be associated with manufacturing step data62 to thereby indicate an association between a particular manufacturingstep identified in the manufacturing step data 62 and one or moresimulated locations on the type of part indicated by the associatedlocation data 60. Building on the example, manufacturing apparatus data64 may be relationally associated with the manufacturing step data 62and the location data 60 to thereby indicate an association between theparticular manufacturing step, the one or more simulated locations, anda particular manufacturing apparatus identified in the manufacturingapparatus data 64. Similarly, possible root cause problem data 72 may berelationally associated manufacturing step data 62 to thereby identifyone or more possible root cause problems that are associated with aparticular manufacturing step identified in the relationally associatedmanufacturing step data 62. As illustrated by these examples, ingeneral, the manufacturing data 32 may indicate relationships betweenthe various types of data, and furthermore, the manufacturing data 32may be associated with a simulated model of the type of part to therebyspatially organize/represent the data on the simulated model of the typeof part. In some embodiments, a display representation of the simulatedmodel and manufacturing data may be generated, and the displayrepresentation may be output on a display for a user.

In general, the routines executed to implement the embodiments of theinvention, whether implemented as part of an operating system or aspecific application, component, algorithm, program, object, module orsequence of instructions, or even a subset thereof, will be referred toherein as “computer program code” or simply “program code.” Program codetypically comprises one or more instructions or sequence of operationsthat are resident at various times in memory and storage devices in acomputer, and that, when read and executed by at least one processor ina computer, cause that computer to perform the steps necessary toexecute steps or elements embodying the various aspects of theinvention. Moreover, while the invention has and hereinafter will bedescribed in the context of fully functioning computers and computersystems, those skilled in the art will appreciate that the variousembodiments of the invention are capable of being distributed as aprogram product in a variety of forms, and that the invention appliesregardless of the particular type of computer readable media used toactually carry out the invention. Examples of computer readable mediainclude, but are not limited to, non-transitory, recordable type mediasuch as volatile and non-volatile memory devices, floppy and otherremovable disks, hard disk drives, tape drives, optical disks (e.g.,CD-ROM's, DVD's, HD-DVD's, Blu-Ray Discs), among others.

In addition, various program code described hereinafter may beidentified based upon the application or software component within whichit is implemented in specific embodiments of the invention. However, itshould be appreciated that any particular program nomenclature thatfollows is merely for convenience, and thus embodiments of the inventionshould not be limited to use solely in any specific applicationidentified and/or implied by such nomenclature. Furthermore, given thetypically endless number of manners in which computer programs may beorganized into routines, procedures, methods, modules, objects, and thelike, as well as the various manners in which program functionality maybe allocated among various software layers that are resident within atypical computer (e.g., operating systems, libraries, ApplicationProgramming Interfaces [APIs], applications, applets, etc.), it shouldbe appreciated that embodiments of the invention are not limited to thespecific organization and allocation of program functionality describedherein.

Those skilled in the art will recognize that the environmentsillustrated in FIGS. 1-2 are not intended to limit the embodiments ofthe invention. Indeed, those skilled in the art will recognize thatother alternative hardware and/or software environments may be usedwithout departing from the scope of the invention.

Software Description and Flows

FIGS. 3-36 provide flowcharts that illustrate sequences of operationsthat may be performed by the computing system 10 and/or user device 12of FIG. 1. In general, the flowcharts of FIGS. 3-36 illustrate operationof possible implementations of systems, methods, and computer programproducts according to various embodiments of the invention. In thisregard, each block in a flowchart may represent a module, segment, orportion of program code, which comprises one or more executableinstructions for implementing the specified logical function(s).Furthermore, any blocks of the below mentioned flowcharts may bedeleted, augmented, made to be simultaneous with another, combined,re-ordered, or be otherwise altered in accordance with the principles ofthe invention.

FIGS. 3-12 provide flowcharts that illustrate a sequence of operationsthat may be performed by the computing system 10 consistent withembodiments of the invention to monitor a manufacturing process thatmanufactures a type of part. Turning now to FIG. 3, which providesflowchart 100, the computing system 10 receives an NDE datasetassociated with a particular part of the type of part (block 102). TheNDE dataset includes a plurality of NDE data points and the NDE datasetcorresponds to data collected during non-destructive evaluation of theparticular part. In general, raw data may be collected by a NDE/Icollection device, and the NDE dataset is based thereon. The NDE datasetis aligned to a simulated model associated with the type of part (block104). In some embodiments, the simulated model may be a simulated modelof the entire type of part; in other embodiments, the simulated modelmay be a portion of the type of part. In general, the computing system10 includes model data 34 upon which the simulated model may be based,and the computing system 10 may align one or more NDE data points of theNDE dataset to the simulated model.

The computing system 10 may analyze one or more aligned NDE data pointsfor one or more locations corresponding to a spatially related featureon the simulated model to determine a spatially correlated statisticthat corresponds to the spatially related feature for the particularpart (block 106). The spatially correlated statistic may be aligned tothe simulated model (block 108). As discussed, the spatially correlatedstatistic corresponds to the spatially related feature, and therefore,aligning the spatially correlated statistic to the simulated model mayinclude aligning the spatially correlated statistic to the simulatedspatially related feature on the simulated model. For example, if thespatially related feature is a defined area on the type of part, thespatially correlated statistic may be aligned to the simulatedrepresentation of the defined area on the simulated model of the type ofpart. The computing system 10 may generate output data based at least inpart on the spatially correlated statistic (block 110). In general, theoutput data may be stored in a memory location associated with thecomputing system 10 and/or communicated by the computing system 10.

In some embodiments of the invention, the model data 34 may store one ormore baseline values associated with the simulated model, where thebaseline values may be indicate a baseline value associated with thesimulated model. In general, the baseline value defines a valueassociated with the type of part that is a target value for the type ofpart by the manufacturing process. In some embodiments the baselinevalue may be spatially correlated such that the baseline value indicatesa target value. For example, the baseline value may indicate a targetaverage thickness for a particular portion of the type of part. Hence,in some embodiments, the computing system 10 may compare the spatiallycorrelated statistic for the particular part to a related baseline valuefor the type of part to determine whether the particular part isacceptable for the type of part (block 112). Continuing the exampleprovided above, the computing system 10 may compare a determined averagethickness for the particular portion of the particular part to thebaseline value, and if the determined average thickness for theparticular part is within a predefined range (e.g., +/−1%) of thebaseline value, the particular part may be determined to be acceptable.

FIG. 4 provides flowchart 120, which illustrates further operations thatmay be performed by the computing system 10 to monitor the manufacturingprocess. Particularly, the computing system 10 may process a pluralityof spatially correlated statistics (block 122), where each spatiallycorrelated statistic corresponds to a manufactured part of the type ofpart manufactured by the manufacturing process. The computing system 10may generate a control chart for the manufacturing process based on thespatially correlated statistics (block 124). In these embodiments, thecontrol chart may indicate the spatially correlated statistic for eachpart manufactured in the manufacturing process. In some embodiments, thecomputing system may generate output data based at least in part on thespatially correlated statistics (block 126), where the output data maybe stored in a memory accessible by the computing system 10 and/orcommunicated by the computing system 10. In some embodiments, thecomputing system may analyze the spatially correlated statistics fromthe parts to determine a manufacturing trend for the manufacturingprocess based at least in part on the spatially correlated statistics(block 128). In these embodiments, the manufacturing trend may indicatea trend of the manufacturing process related to the spatially correlatedstatistics. As each spatially correlated statistic is related to aspatially related feature, the manufacturing trend may thereby indicatea trend associated with the spatially related feature for themanufacturing process. For example, if each spatially correlatedstatistic may correspond to an average thickness for a particularportion of the corresponding part, the manufacturing trend maycorrespond to the variability in the average thickness for the portionof each part.

FIG. 5 provides flowchart 140, which illustrates further operations thatmay be performed by the computing system 10 to monitor the manufacturingprocess. In some embodiments, a manufacturing trend and/or control chartbased on spatially correlated statistics for a plurality of partsmanufactured by the manufacturing process may be processed (block 142).The computing system 10 may analyze the manufacturing trend and/or thecontrol chart to determine whether the manufacturing process isoperating properly (block 144). As discussed previously, themanufacturing trend may correspond to a spatially related feature forthe type of part manufactured by the manufacturing process. Hence, basedon the manufacturing trend, the computing system may determine whetherthe spatially related feature of the manufactured parts indicate thatthe manufacturing process is operating properly with respect to thespatially related feature.

For example, continuing the average thickness example from above, if anacceptable range of average thickness for the particular portion isdefined for the type of part, the computing system 10 may analyze themanufacturing trend to determine if, based on the manufacturing trend,the manufacturing process is likely to begin producing parts having anaverage thickness for the particular portion not in the acceptablerange. In this example, the average thickness for each manufactured partmay be within the acceptable range, but the manufacturing trend mayindicate that out-of-range parts are likely to be produced. If theaverage thickness of the particular portion of each part is increasingfor each later manufactured part, even if the particular portion of eachmanufactured part is within the acceptable range, the computing systemmay determine that the manufacturing process is not operating properlybecause subsequently produced parts will exceed the maximum acceptablelimit of the acceptable range based on the manufacturing trend.

Returning to FIG. 5, in response to determining that the manufacturingprocess is operating properly (‘Y’ branch of block 146), the computingsystem 10 continues analyzing the manufacturing trend. While thedescription and flowcharts may describe receiving NDE datasets receivingNDE datasets, determining spatially correlated statistics, determining amanufacturing trend, analyzing the manufacturing trend to determinewhether the manufacturing process is operating properly, etc. as asingle occurrence, the invention is not so limited. In some embodiments,the receipt of NDE datasets, processing of the NDE datasets, arecontinuous, such that as parts may be manufactured by the manufacturingprocess, the computing system 10 continues to process received NDE dataand/or quality related data. As such, as shown in FIG. 5, when thecomputing system 10 determines that the manufacturing process isoperating properly, the computing system 10 continues analyzing themanufacturing trend, as data is received and processed for partsmanufactured by the manufacturing process. By continuously receiving andprocessing data associated with the manufactured parts, embodiments ofthe invention continuously monitor the manufacturing process such thatany potential problems that may arise in the manufacturing process maybe detected in a timely manner.

In response to determining that the manufacturing process is notoperating properly (‘N’ branch of block 146), the computing system maydetermine a root cause problem associated with the manufacturing process(block 148). In general, the root cause problem may be determined from aplurality of possible root cause problems associated with themanufacturing process, the spatially correlated statistics, the NDEdatasets, and/or the type of part. In some embodiments, the computingsystem 10 may receive user input data that identifies the root causeproblem associated with the manufacturing process to thereby determinethe root cause problem. The root cause problem may correspond to one ormore aspects of the manufacturing process, where such aspects generallydepend on the type of part and the manufacturing process. For example,if a manufacturing process manufactures molded parts, and a spatiallycorrelated statistic determined for each of a plurality of manufacturedparts is the average porosity of a portion of each part, if the averageporosity of manufactured parts is increasing over time, a root causeproblem associated with the manufacturing process may be the wearing ofa gasket for a mold used in the manufacturing process.

Based on the determined root cause problem, the manufacturing trend, thespatially correlated statistics, and/or the NDE datasets, the computingsystem 10 may generate spatially correlated manufacturing data thatidentifies the determined root cause problem (block 150). In someembodiments, the computing system may determine a manufacturing stepthat corresponds to the root cause problem (block 152). For example, ifthe manufacturing process comprises a plurality of manufacturing steps,such as molding, curing, and cutting a type of part, the computingsystem may determine a particular manufacturing step that corresponds tothe root cause problem.

FIG. 6 provides flowchart 160 that illustrates further operations thatmay be performed by the computing system 10 to monitor the manufacturingprocess. As shown, manufacturing data for a type of part may beprocessed (block 162), where the manufacturing data may indicate atleast one problem that is associated with at least one correspondingsimulated location on the simulated model. The manufacturing data may beassociated with the simulated model (block 164), and simulated locationsthat have related indicated problems may be identified by the computingsystem 10 (block 166). Based on the identified simulated locations, thecomputing system 10 may determine an area of interest associated withthe type of part (block 168). The computing system 10 may communicatedata that identifies the area of interest for the type of part.

An area of interest for a type of part may define a part, a particularportion of the type of part, an area, a region, a volume, and/or othersuch spatially related feature of the type of part. In general, an areaof interest may be utilized by embodiments of the invention to define aportion or other such spatially related feature that particular interestshould be paid when inspecting each part of the type of part, or forwhich NDE data and/or other quality related data should be collected.Such spatially related features may include, for example, a seam on acomposite part that corresponds to a seam in a mold for the compositepart, a portion of a part proximate a cut, weld, securing element,bonded portion, and/or other such types of spatially related features.In addition, an area of interest may be defined on the simulated modeland used to filter data on the simulated model, such that data notcorresponding to the area of interest may be filtered from the simulatedmodel.

Turning now to FIG. 7, which provides flowchart 180, as shown, thecomputing system 10 may analyze manufacturing data associated with thetype of part to determine an area of interest for the type of part(block 182). As discussed previously, manufacturing data may indicateone or more potential problems associated with the type of part, wheresuch potential problems may have been derived from NDE data and/orquality related data for manufactured parts of the type of part. Inthese embodiments, the computing system 10 may analyze the manufacturingdata to determine an area of interest for the type of part, where sucharea of interest may correspond to a plurality of indications ofpotential problems. The computing system 10 may select NDE data that isaligned to simulated locations on the simulated model that areassociated with the determined area of interest (block 184), and thecomputing system 10 may determine a spatially correlated statistic forthe area of interest based on the selected NDE data (block 186).

FIG. 8 provides flowchart 200 that illustrates a sequence of operationsthat may be performed by the computing system 10 consistent withembodiments of the invention when processing the simulated modelincluding and aligned NDE data points (block 201). The computing systemmay generate a display representation of the simulated model (block202). The display representation may be displayed for a user via thecomputing system 10 and/or the user device 12, and the computing system10 may receive user input data that indicates and area of interest onthe display representation (block 204). In general, the user mayinterface with the computing system 10 and/or user device 12 executingan application that allows the user to provide input data related to thedisplay representation via one or more input devices. The computingsystem 10 and/or user device 12 may communicate data that identifies thearea of interest for the display representation (block 206), and thecomputing system 10 may receive manufacturing data associated with thetype of part (block 208). The computing system may associatemanufacturing data with the simulated model (block 210), where themanufacturing data indicates at least one problem associated with one ormore simulated locations of the simulated model. The computing system 10may update the display representation such that visual representationsof problems associated with the area of interest may be included in thedisplay representation of the simulated model (block 212).

FIG. 9 provides flowchart 220 that illustrates operations that may beperformed by the computing system 10 when processing NDE data for thetype of part (block 221). The computing system 10 aligns NDE data pointsof the NDE data to a simulated model of the type of part (block 222),and the computing system 10 analyzes the aligned NDE data points todetermine one or more spatially correlated statistics (block 224). Thecomputing system 10 aligns the one or more spatially correlatedstatistics to the simulated model (block 226), and the computing system10 may generate a display representation of the simulated model thatvisually represents the one or more spatially correlated statistics onthe simulated model of the type of part (block 228).

FIG. 10 provides flowchart 240 that illustrates operations that may beperformed by the computing system 10 when processing NDE data for a typeof part (block 242). The computing system 10 may align NDE data pointsof the NDE data to a simulated model of the type of part (block 244).The computing system 10 may analyze the aligned NDE data points todetermine whether the aligned NDE data points indicate a potentialproblem at a corresponding location on a particular part associated withthe NDE data point (block 246). In response to determining that analigned NDE data point indicates a potential problem at a correspondinglocation on the particular part associated with the NDE data point (‘Y’branch of block 246), the computing system 10 aligns an indication ofthe potential problem to the simulated model (block 248). The computingsystem 10 analyzes aligned indications of potential problems on thesimulated model to determine an area of interest for the type of part(block 250). The computing system 10 may generate a displayrepresentation of the simulated model that visually representsindications of problems aligned to the simulated model (block 252). Ifthe computing system 10 does not determine that any aligned NDE datapoints indicate a potential problem (‘N’ branch of block 246), thecomputing system 10 may generate the display representation without anyindications (block 252).

FIG. 11 provides flowchart 260 that illustrates a sequence of operationsthat may be performed by the computing system 10 when processing thesimulated model of the type of part that includes aligned indications ofpotential problems (block 262). In some embodiments, the computingsystem may generate a display representation of the simulated model thatvisually represents the aligned indications of potential problems (block264), and the computing system 10 may receive user input data thatindicates an area of interest for the type of part based on the displayrepresentation (block 266).

FIG. 12 provides flowchart 280 that illustrates operations that may beperformed by the computing system 10 consistent with embodiments of theinvention to monitor the manufacturing process. The computing system mayreceive an NDE dataset for each of a plurality of parts of a type ofpart (block 282). The computing system 10 may align the NDE datasets toa simulated model of the type of part (block 284), and the computingsystem 10 may analyze at least a subset of the data points for each NDEdataset to determine a spatially correlates statistic for each part(block 286). Based on the spatially correlated statistics, the computingsystem 10 may generate a control chart that includes each spatiallycorrelated statistic (block 288). In some embodiments, the computingsystem 10 may determine a manufacturing trend for the type of part basedon each spatially correlated statistic (block 290), and the computingsystem may determine an area of interest for the type of part based onthe manufacturing trend (block 292). In some embodiments, the computingsystem 10 may generate a display representation of the simulated modelthat visually represents the spatially correlated statistics on thesimulated model (block 294).

FIGS. 13-16 provide flowcharts that illustrate sequences of operationsthat may be performed by the computing system 10 and/or user device 12consistent with embodiments of the invention to monitor manufacture of atype of part by a manufacturing process that includes one or moremanufacturing steps. Specifically, referring to FIG. 13, which providesflowchart 300, the computing system 10 may receive an NDE dataset foreach of a plurality of parts of the type of part, where each NDE datasetis associated with an area of interest for the type of part (block 302).The computing system 10 aligns the NDE data points of each NDE datasetto a simulated model of the area of interest for the type of part (block304), and the computing system 10 analyzes the aligned NDE data pointsfor each part to determine a statistic associated with the area ofinterest for each part (i.e., a spatially correlated statistic) (block306). The computing system 10 may align each statistic to the simulatedmodel (block 308), and generate a display representation of thesimulated model that visually represents each aligned statistic on thesimulated model (block 310). In some embodiments, the computing system10 may generate a control chart for the manufacturing process associatedwith the area of interest for the type of part (block 312).

Turning now to FIG. 14, this figure provides flowchart 320, whichillustrates operations that the computing system 10 may perform whenprocessing the simulated model of the area of interest that includesaligned statistics for the area of interest (block 322). The computingsystem 10 may determine a manufacturing trend for the manufacturingprocess associated with the area of interest (block 324). In someembodiments the computing system may generate a display representationof the simulated model of the area of interest that visually representsthe manufacturing trend on the simulated model (block 326).

In some embodiments of the computing system may analyze themanufacturing trend and base line data associated with the simulatedmodel of the type of part to determine whether the manufacturing processis operating properly (block 328). In response to determining that themanufacturing process is operating properly (‘Y’ branch of block 330),the computing system 10 may continue analyzing the manufacturing trendas the manufacturing trend updates based on received NDE data. Inresponse to determining that the manufacturing process is not operatingproperly (‘N’ branch of block 330), the computing system 10 maydetermine a root cause problem for the manufacturing process associatedwith the area of interest (block 332).

FIG. 15 provides a flowchart 340 that illustrates operations that thecomputing system 10 may perform when processing the simulated model ofthe area of interest including aligned statistics associated with thearea of interest (block 342). The computing system 10 may receivemanufacturing data (block 344), where the manufacturing data indicatesone or more possible root cause problems associated with the area ofinterest. In some embodiments, the manufacturing data may correspond tothe type of NDE data from which the aligned statistics were determined.For example, if the NDE data corresponded to measured porosity valuesand the statistic associated with the area of interest for each part wasan average porosity, the manufacturing data may indicate possible rootcause problems associated with porosity values. The computing system mayanalyze the manufacturing data and the aligned statistics to determine aroot cause problem associated with the area of interest (block 348).

In some embodiments of the invention, the manufacturing data may furtherindicate one or more manufacturing steps, one or more manufacturingapparatuses, one or more manufacturing tools, and/or one or moremanufacturing parameters associated with the area of interest, the rootcause problem, and/or the other types of indicated data. Therefore,consistent with these embodiments of the invention, the computing system10 may determine a manufacturing step associated with the root causeproblem and/or area of interest (block 350). Similarly, the computingsystem 10 may determine a manufacturing apparatus associated with theroot cause problem, the area of interest, and/or the determinedmanufacturing step (block 352). In addition, the computing device 10 maydetermine a manufacturing tool associated with the root cause problem,the area of interest, the determined manufacturing step, and/or thedetermined manufacturing apparatus (block 354). Furthermore, thecomputing device 10 may determine a manufacturing parameter associatedwith the root cause problem, the area of interest, the manufacturingstep, and/or the manufacturing apparatus (block 356).

FIG. 16 provides flowchart 360 that illustrates operations that may beperformed by the computing system 10 after a process adjustment isimplemented for the manufacturing process (block 362). The computingsystem 10 receives one or more NDE datasets collected from one or moreparts manufactured after implementation of the process adjustment (block364). The computing system 10 aligns the one or more NDE datasets to thesimulated model (block 366), and the computing system analyzes thealigned NDE datasets to determine a statistic for the area of interest(i.e., a spatially correlated statistic) for each part manufacturedafter the process adjustment implementation (block 368). The computingsystem 10 may evaluate the process adjustment to determine whether theprocess adjustment corresponds to the root cause problem by analyzingthe statistics for the area of interest for each part manufactured afterthe process adjustment and the statistics for the area of interest foreach part manufactured before the process adjustment (block 370). Insome embodiments, the computing system 10 may further determine theextent to which the process adjustment affected the root cause problem,where the extent may be defined based at least in part on the differencein the statistics for the area of interest for the parts manufacturedafter the process adjustment and the statistics for the area of interestfor the parts manufactured before the process adjustment.

FIG. 17 provides a flowchart 380 that illustrates a sequence ofoperations that may be performed by the computing system 10 consistentwith embodiments of the invention to monitor the manufacture of a typeof part by a manufacturing process that includes a plurality ofmanufacturing steps. The computing system 10 receives an NDE dataset foreach of a plurality of parts of the type of part manufactured by themanufacturing process, where the NDE dataset for each part is associatedwith an area of interest for the type of part (block 382). The computingsystem 10 aligns the NDE datasets to a simulated model of the area ofinterest for the type of part (block 384), and the computing systemanalyzes the aligned NDE datasets detect any sub-rejectable physicalcharacteristic(s) associated with the area of interest for each part. Ingeneral, a sub-rejectable physical characteristic refers to a physicalcharacteristic that is within an acceptable range for the type of part,but that is outside an expected range (i.e., the sub-rejectable physicalcharacteristic is acceptable but outside the range associated with noisein the manufacturing process). In some embodiments pre-defined valuesassociated with the simulated model may define sub-rejectable ranges,where such the sub-rejectable range may be proximate a minimum ormaximum limit of the acceptable range and/or not be within atypical/expected range. In general, an NDE data point may indicate ameasured value for a location on a part from which the NDE data pointwas collected, and if the measured value is proximate a limit associatedwith an acceptable range for the value, embodiments of the invention mayidentify the location on as a sub-rejectable physical characteristic.Moreover, the model data of the simulated model may define values thatcorrespond to sub-rejectable physical characteristics.

In some embodiments of the invention, the computing system may align anindication of each detected sub-rejectable physical characteristic to acorresponding simulated location on the simulated model (block 388). Inaddition, the computing system 10 may generate a control chart thatincludes indications for each detected sub-rejectable for the type ofpart (block 390). The computing system 10 may analyze the control chartand/or aligned indications to determine whether a potential problem isoccurring for the manufacturing process (block 392). The computingsystem 10 may determine a manufacturing trend for the manufacturingprocess based at least in part on the aligned indications and/or thecontrol chart (block 394). In some embodiments, the computing system 10may analyze the control chart, one or more baseline values associatedwith the area of interest, and/or the manufacturing trend to determinewhether the control chart indicates a potential problem in themanufacturing process (block 396). As discussed previously, a potentialproblem may be indicated by data that indicates that the manufacturingprocess is manufacturing parts that are trending towards a limit of anacceptable range for one or more physical characteristics. Hence, whilethe manufacturing process may be manufacturing acceptable parts, basedon the NDE data and/or quality related data for each manufactured part,the computing system 10 may determine that a potential problem isoccurring in the manufacturing process.

FIG. 18 provides a flowchart 420 that illustrates a sequence ofoperations that may be performed by the computing system 10 to model themanufacture of a type of part by a manufacturing process that includesat least one manufacturing step. The computing system may receive atleast one NDE dataset for at least one part of the type of part (block422), and the computing system 10 may align the NDE dataset to asimulated model of the type of part (block 424). The computing systemmay receive manufacturing data associated with the manufacturing process(block 426) and associate the manufacturing data with the simulatedmodel (block 428). Based on the simulated location of aligned NDE data,manufacturing data may be associated with particular NDE data (block430), such that the computing system may: identify a manufacturing stepassociated with particular NDE data aligned to one or more particularsimulated locations (block 432); identify a manufacturing apparatusassociated with particular NDE data aligned to one or more particularsimulated locations (block 434); identify a manufacturing toolassociated with particular NDE data aligned to one or more particularsimulated locations (block 436); and/or generate a displayrepresentation of the simulated model that visually represents at leastsome aligned NDE data and manufacturing data associated therewith (block438). Hence, in these embodiments, data associated with aspects of themanufacturing process may be spatially organized on a simulated model ofthe type of part, such that the data associated with the manufacturingprocess may be spatially correlated with NDE data and/or quality relateddata collected from one or more parts manufactured by the manufacturingprocess. Therefore, consistent with these embodiments of the invention,the manufacturing process may be modeled on the simulated model of thetype of part.

Turning now to FIG. 19, this figure provides a flowchart 460 thatillustrates a sequence of operations that may be performed by thecomputing system 10 to analyze a manufactured part of a type of partmanufactured by a manufacturing process. The computing system receivesan NDE dataset associated with the manufactured part (block 462) andaligns the NDE dataset to a simulated model associated with the type ofpart (block 464), where such aligning includes aligning NDE data of theNDE dataset associated with an area of interest on the manufactured partto at least one corresponding simulated location on the simulated model.The computing system 10 may analyze the NDE data aligned to the area ofinterest to determine a spatially correlated statistic for the area ofinterest for the manufactured part (block 466). The computing system 10may compare the spatially correlated statistic to a baseline valueassociated with the area of interest (block 468). Based at least in parton the NDE data aligned to the area of interest and/or the comparison ofthe spatially correlated statistic to the baseline value, the computingsystem 10 may determine whether the manufactured part includes amanufacturing defect associated with the area of interest (block 470).In general, a manufacturing defect corresponds to a physicalcharacteristic that is not within an acceptable range, where theacceptable range is predefined. If a manufacturing defect is notdetected for the manufactured part (‘N’ branch of block 470), then theanalysis process ends (block 472).

In response to detecting a defect for the manufactured part (‘Y’ branchof block 470), the computing system 10 may align the detected defect tothe simulated model (block 474). The computing system 10 may analyzemanufacturing data associated with the simulated location of the aligneddefect to determine a root cause problem associated with the simulatedlocation and/or detected defect (block 478).

In addition, in response to detecting a defect for the manufactured part(‘Y’ branch of block 470), the computing system 10 may determine amanufacturing step associated with the defect based at least in part onthe simulated location of the aligned defect (block 480). Similarly, thecomputing system 10 may determine a manufacturing apparatus associatedwith the defect based at least in part on the simulated location of thealigned defect and/or the determined manufacturing step (block 482).Furthermore, the computing system 10 may determine a manufacturing toolassociated with the defect based at least in part on the simulatedlocation of the aligned defect, the determined manufacturing step,and/or the determined manufacturing apparatus.

Referring to FIG. 20, this figure provides flowchart 500 thatillustrates a sequence of operations that may be performed by thecomputing system 10 to monitor the manufacture of composite aircraftparts of a type of part by a manufacturing process. In general, theproduction of composite aircraft parts may be a complicated andexpensive process, where producing even one defective part may result insignificant time and cost losses. Therefore, in this embodiment of theinvention, the manufacturing process is monitored continuously as NDEdata and/or quality related data is collected from one or more of theaircraft parts during and immediately following manufacture of eachpart. In this manner, embodiments of the invention may monitor whetherthe manufacturing process is operating properly to reduce theprobability of time and cost losses due to the development of a problemin the manufacturing process. As discussed, the NDE datasets arereceived continuously, and processing and analysis based thereon isperformed in a continuous manner. Hence, flowchart 500 may be considereda snapshot of the continuously performed operations consistent with someembodiments of the invention.

The computing system 10 receives NDE datasets for each of a plurality ofcomposite aircraft parts manufactured in the manufacturing process(block 502). The computing system 10 aligns the received NDE datasets toa simulated model of the type of part (block 504), and the computingsystem 10 analyzes the aligned NDE datasets to determine a spatiallycorrelated statistic for each composite aircraft part of the type (block506). The computing system 10 aligns the spatially correlated statisticsto the simulated model (block 508). In some embodiments, the computingsystem 10 receives manufacturing data associated with the type of part(block 510), and the computing system 10 associates the manufacturingdata with the simulated model (block 512). The manufacturing data mayinclude data that indicates: at least one manufacturing step of themanufacturing process associated with one or more physical locations onthe type of part, data that indicates a manufacturing apparatus utilizedin the manufacturing process associated with at least one physicallocation on the type of part; a manufacturing parameter of themanufacturing process associated with at least one physical location onthe type of part; a manufacturing tool utilized in the manufacturingprocess associated with at least one physical location on the type ofpart; at least one possible root cause problem associated with themanufacturing process and at least one physical location on the type ofpart.

The computing system may generate a display representation of thesimulated model that visually represents the spatially correlatedstatistics, manufacturing data, and/or NDE data of the NDE datasetsaligned on the simulated model (block 514). In some embodiments, thecomputing system 10 determines a manufacturing trend for themanufacturing process based at least in part on the spatially correlatedstatistics (block 516), and the computing system may analyze themanufacturing trend, NDE data, and/or baseline data associated with thesimulated model to determine whether the manufacturing process isoperating properly (blocks 518-520). In response to determining that themanufacturing process is operating properly (‘Y’ branch of block 520),the computing system 10 continues monitoring the manufacturing process.In response to determining that the manufacturing process is notoperating properly (‘N’ branch of block 522), the computing system 10may generate output data that indicates that the manufacturing processis not operating properly and/or the computing system 10 may determine aroot cause problem associated with the manufacturing process based atleast in part on the spatially correlated statistics, manufacturingtrend, and/or manufacturing data (block 522). In general, the outputdata may be communicated such that an alarm or other such notificationis generated for an operator/technician/supervisor associated with themanufacturing process.

FIG. 21 provides a flowchart 540 that illustrates a sequence ofoperations that may be performed by the computing system 10 to analyzemanufacture of a type of part by a manufacturing process. The computingsystem may receive an NDE dataset for each part of a plurality of partsof the type of part (block 542). The computing device 10 aligns the NDEdatasets to a simulated model associated with the type of part (block544) and analyzes the aligned NDE datasets (block 546) to determinewhether the manufacturing process is operating properly (block 548). Inresponse to determining that the manufacturing process is operatingproperly (‘Y’ branch of block 548), the computing system 10 may continueanalyzing the manufacturing process as NDE datasets are received.

In response to determining that the manufacturing process is notoperating properly (‘N’ branch of block 548), the computing system 10may determine a root cause problem associated with the manufacturingprocess based at least in part on the aligned NDE data (block 550).Furthermore, the computing system 10 may identify one or more otheraspects of the manufacturing process based on the aligned NDE data,including at least one manufacturing step (block 552), at least onemanufacturing parameter associated with the manufacturing step (block554), at least one manufacturing apparatus (block 556), and/or at leastone manufacturing tool (558). In some embodiments, the computing systemmay determine the root cause problem based at least in part on the oneor more identified aspects of the manufacturing process. In someembodiments, the computing system 10 may generate output data responsiveto determining that the manufacturing process is not operating properly(block 560). The output data may be communicated to provide anotification that the manufacturing process is not operating properly,and the output data may include the determined root cause problem and/orone or more identified manufacturing aspects.

FIG. 22 provides a flowchart 580 that illustrates a sequence ofoperations that may be performed by the computing system 10 to analyzethe manufacture of a type of part by a manufacturing process. Thecomputing system may receive NDE data associated with the type of part,where the NDE data includes associated inspection information (block582). The computing system may automatically align the NDE dataincluding the inspection information to corresponding simulatedlocations on a simulated model associated with the type of part (block584), and the computing system may analyze the aligned NDE data tomonitor the manufacturing process and determine whether themanufacturing process is operating properly (block 586).

FIG. 23 provides a flowchart 600 that illustrates a sequence ofoperations that the computing system may perform to monitor themanufacturing process. When the monitor is initialized (block 602), thecomputing system 10 determines whether the aligned NDE data and/orinspection information indicates a potential problem associated with themanufacturing process (blocks 604-606). In response to determining thatthe aligned NDE data and/or the inspection information does not indicatea potential problem (‘N’ branch of block 606), the computing system 10continues analyzing the NDE data and/or inspection information as it isreceived and aligned.

In response to determining that the aligned NDE data and/or inspectioninformation indicates a potential problem (‘Y’ branch of block 606), thecomputing system 10 may align an indication of the potential problem tothe simulated model (block 608). The computing system 10 may generate adisplay representation of the simulated model that visually representsthe aligned indication on the simulated model (block 610). In someembodiments the computing system 10 may analyze the one or more alignedindications to determine a root cause problem associated with themanufacturing process and the aligned indication (block 612).

FIG. 24 provides flowchart 640 that illustrates a sequence of operationsthat the computing system may perform to determine a root cause problembased on a plurality of indications of potential problems aligned to thesimulated model (block 642). The computing system 10 may analyze thecorresponding simulated locations of the simulated model to which theindications are aligned to identify a pattern for the correspondinglocation (block 644). The computing system 10 may determine a root causeproblem for the manufacturing process based at least in part on theidentified pattern (block 646).

FIG. 25 provides flowchart 660 that illustrates a sequence of operationsthat may be performed by the computing system 10 consistent withembodiments of the invention when processing the simulated model withaligned indications of potential problems (block 662). The computingsystem 10 may analyze manufacturing data associated with the one or moresimulated locations of the one or more aligned indications (block 664).In some embodiments, the computing system 10 may suggest one or morepossible root cause problems based at least in part on the manufacturingdata (block 668). In these embodiments, the computing system 10 mayoutput data via a user interface to a user, where the output dataincludes the one or more suggested possible root cause problems. Thecomputing system receives user input data that selects one or more rootcause problems from the suggested possible root cause problems (block670), and the computing system 10 may associate the one or more selectedroot cause problems with one or more aligned indications (block 672). Insome embodiments, the computing system 10 may generate and/or updatemanufacturing data associated with the type of part based at least inpart on the one or more selected root cause problems, the associatedaligned indications, and/or the simulated locations (block 674).Consistent with some embodiments, the computing system 10 may determinea root cause problem associated with the manufacturing process based onmanufacturing data associated with the simulated model, one or morealigned indications, simulated locations of the aligned indications,and/or aligned NDE data (block 676).

Turning now to FIG. 26, this figure provides a flowchart 700 whichillustrates a sequence of operations that may be performed by thecomputing system 10 to analyze manufacture of a type of part by amanufacturing process. The computing system receives NDE data thatincludes inspection information and a plurality of indications of one ormore potential problems (block 702) for a part of the type of part, andthe computing system 10 may align the NDE data to a simulated modelassociated with the type of part (block 704). The computing systemgenerates a display representation of the simulated model that visuallyrepresents the aligned indications on the simulated model (block 706),and the computing system 10 may receive data that indicates a root causeproblem associated with at least one particular indication (block 708).The computing system associates the root cause problem with the at leastone particular indication (block 710), and the computing system maygenerate manufacturing data based on the at least one particularindication and the root cause problem (block 712).

FIG. 27 provides a flowchart 720 that illustrates operations that may beperformed by the computing system 10 to analyze the manufacturingprocess based at least in part on the simulated model, aligned NDE data,and manufacturing data of FIG. 26 (block 722) (i.e., first NDE data froma first part). The computing system 10 may receive second NDE data thatincludes inspection information and at least one indication of at leastone potential problem for a second part of the type of part (block 724),and the computing system aligns the NDE data to the simulated model(block 726). The aligned second NDE data and aligned indication may beanalyzed by the computing system 10 to determine whether the aligned atleast one indication of the second NDE data is related to anyindications of the first NDE data based at least in part on themanufacturing data (blocks 728-730). In response to determining that oneor more indications of the second NDE data are related to one or moreindications of the first NDE data (‘Y’ branch of block 730), thecomputing system may associate the root cause problem associated withthe one or more related indications of the first NDE data to the one ormore related indications of the second NDE data (block 732). If the oneor more indications of the second NDE data are not determined to berelated to any indications of the first NDE data, the computing system10 may process the indications of the second NDE as described above withrespect to FIG. 26 to determine an associated root cause problem (block734).

FIG. 28 provides flowchart 760 that illustrates operations that may beperformed by the computing system 10 to determine a root cause problemassociated with NDE data and/or one or more indications of one or morepotential problems aligned to the simulated model of FIG. 26 (block762). The computing system 10 may analyze the aligned NDE data and/orindications and manufacturing data associated with the simulatedlocations to which the NDE data and/or indications are aligned todetermine a root cause problem associated with each of the one or morealigned indications (block 764). The computing system 10 may generatedata that indicates the determined root cause problem and the associatedone or more indications (block 766).

Turning now to FIG. 29, this figure provides flowchart 800 thatillustrates a sequence of operations that may be performed by thecomputing system 10 to model the manufacture of a type of part by amanufacturing process. The computing system 10 may analyze NDE dataassociated with the manufacturing process to determine indications ofpotential problems associated with the manufacturing process (block802), and the computing system 10 aligns the NDE data and indications toa simulated model associated with the type of part (block 804). Thecomputing system 10 may receive request data that indicates a root causeproblem associated with the manufacturing process (block 806). Thecomputing system 10 identifies aligned indications associated with theroot cause problem (block 808), and the computing system 10 filters anyindications not associated with the root cause problem out of thesimulated model (block 810). In some embodiments, the computing system10 may analyze the aligned NDE data to determine a spatially correlatedstatistic associated with the root cause problem (block 812).Furthermore, the computing system may generate a display representationof the simulated model that visually represents the aligned indicationsand/or spatially correlated statistic associated with the root causeproblem on the simulated model (block 814).

FIG. 30 provides a flowchart 840 that illustrates operations that may beperformed by the computing system 10 when processing the simulated modeland the aligned NDE data of FIG. 29 (block 842). The computing system 10may generate a display representation of the simulated model thatvisually represents the aligned indications on the simulated model(block 844). The computing system 10 may receive user input data thatindicates a root cause problem for the manufacturing process (block846), and the computing system 10 may generate request data thatindicates the root cause problem based on the user input data (block848). The computing system may update the display representation basedon the request data such that the display representation visuallyrepresents only indications associated with the root cause problem(block 850).

Turning to FIG. 31, this figure provides a flowchart 860 thatillustrates operations that may be performed by the computing system 10when processing the simulated model and the aligned NDE data of FIG. 29(block 862). The computing system 10 may receive manufacturing dataassociated with the manufacturing process (block 864) and associate themanufacturing data with one or more simulated locations on the simulatedmodel (block 866). The computing system may determine one or moreindications and manufacturing data associated with the root causeproblem of the received request of FIG. 29 (block 868), and thecomputing system 10 may filter the simulated model, aligned indications,and the manufacturing data to remove the aligned indications andmanufacturing data not associated with the root cause problem (block870).

Referring to FIG. 32, this figure provides a flowchart 880 thatillustrates a sequence of operations that may be performed by thecomputing system 10 to monitor a manufacturing process that manufacturesa type of part. The computing system 10 may receive non-compliancereport data for each of a plurality of parts manufactured by themanufacturing process (block 882), where each non-compliance reportindicates at least one visually detected defect corresponding to alocation on the respective part that is associated with thenon-compliance report. In general, a non-compliance report and visuallydetected defect thereof may not be as location specific as NDE data;hence, in many embodiments the particular location of the visuallydetected defect may correspond to an area, volume, region, and/or othersuch spatially related feature. The computing system 10 aligns eachvisually detected defect to a simulated model of the type of part (block884), where each visually detected defect is aligned to one or moresimulated locations on the simulated model that correspond to theparticular location of the visually detected defect on the respectivepart. The computing system 10 may monitor the manufacturing process byanalyzing the aligned visually detected defects (block 886).

In some embodiments, the computing system 10 may generate a displayrepresentation of the simulated model that includes the one or morealigned visually detected defects (block 888). In addition, thecomputing system 10 may generate manufacturing data based at least inpart on the aligned visually detected defects (block 890). Furthermore,the computing system may determine whether a manufacturing problem isoccurring (block 892). In general, if the computing system 10 detects aplurality of visually detected defects aligned to common and/or relatedsimulated locations, the computing system 10 may determine that amanufacturing problem is occurring.

With reference to FIG. 33, this figure provides a flowchart 920 thatillustrates a sequence of operations that may be performed by thecomputing system 10 to monitor a manufacturing process that manufacturesa type of part. The computing system 10 may receive indication data thatincludes indications of potential problems at locations on parts of thetype of part manufactured by the manufacturing process (block 922), andthe computing system aligns the indications to a simulated model of thetype of part (block 924), where each indication is aligned to one ormore simulated locations on the simulated model that correspond to thelocation on the part at which the potential problem was detected. Thecomputing system 10 may receive manufacturing data associated with themanufacturing process that includes one or more possible root causeproblems associated with the manufacturing process (block 926). Thecomputing system may analyze the aligned indications and the receivedmanufacturing data and associate the manufacturing data with the alignedindications (block 928). In some embodiments, the computing system 10may generate a display representation of the simulated model thatvisually represents the aligned indications and/or the manufacturingdata with the simulated model (block 930).

FIG. 34 provides a flowchart 940 that illustrates operations thecomputing system 10 may perform when processing the displayrepresentation of the simulated model including the aligned indicationsof FIG. 33 (block 942). The computing system 10 may receive user inputdata that selects a particular root cause problem (block 946), and thecomputing system 10 may filter the display representation to removeindications not associated with the selected root cause problem (block948). In other embodiments, the computing system 10 may filter thedisplay representation to remove indications associated with theselected root cause problem (block 950).

FIG. 35 provides a flowchart 940 that illustrates operations thecomputing system 10 may perform related to the operations of FIG. 33,where the aligned indications of FIG. 33 are a first set of alignedindications, when processing the display representation of the simulatedmodel including the aligned indications of FIG. 33 (block 962). Thecomputing system 10 may receive second indication data including asecond set of indications of potential problems at locations on parts ofthe type of part (block 964), and the computing system 10 may align thesecond set of indications (block 966). Based on the root cause problemassociated with the first set of aligned indications and the simulatedlocations thereof, the computing system 10 may generate data thatsuggests a possible root cause problem (i.e., a root cause hypothesis)for at least one indication of the second set of aligned indications(block 968). Hence, the computing system may rely on previouslyidentified root cause problems to determine possible root cause problemsfor received indications of potential problems.

FIG. 36 provides a flowchart 980 that illustrates operations thecomputing system 10 may perform when processing the displayrepresentation of the simulated model including the aligned indicationsof FIG. 33 (block 982). The computing system 10 may receive user inputdata that identifies a root cause problem associated with one or moreidentified aligned indications (block 984), and the computing system 10may associate the identified root cause problem to the identified one ormore aligned indications (block 986). In some embodiments, the computingsystem 10 may filter the display representation to remove alignedindications associated with the identified root cause problem from thedisplay representation (block 988).

Turning now to FIG. 37, this figure provides an example illustration ofa graphical user interface (GUI′) 1000 that may be output to a displayby a processor executing an application on the user device 12 and/orcomputing system 10. In this example, the GUI 1000 includes a threedimensional generated display representation of a simulated model of atype of composite aircraft part 1002. In this example, the GUI 1000 mayinclude the display representation 1002 as well as facilitate userinterface with the display representation to input data (such asselecting an area of interest on the display representation) via one ormore included interface features 1004 (i.e., selection buttons, textinput boxes, etc.). FIG. 38 provides an example illustration of the GUI1000 of FIG. 37, where the generated display representation 1002 of FIG.38 includes aligned NDE data 1006, which in the example, corresponds toaligned ultrasonic scan data that may be utilized to determine aporosity value and/or other such physical characteristic at a locationof a part. FIGS. 39A-C provide an example illustration of the GUI 1000of FIG. 38, where the generated display representation 1002 of FIGS.39A, B includes aligned indications of potential problems 1008. FIG. 39Bprovides a close up view of the selected area 1010 of FIG. 39A to betterillustrate the aligned indications 1008. FIG. 39C provides the interfacefeatures 1004 portion of the GUI 1000, where the interface features 1004portion of the GUI 1000 includes information related to the alignedindications 1012. FIG. 40 provides an example illustration of the GUI1000 of FIGS. 37-39A-C, where the user is interfacing with the GUI toselect an area of interest 1014 on the display representation 1002.

FIGS. 41A-B provide an example illustration of a GUI 1050 that may beoutput to a display by a processor executing an application on the userdevice 12 and/or computing system 10. In this example, the GUI 1050includes a generated display representation of a simulated model of atype of composite aircraft part 1052 manufactured by a manufacturingprocess. In this example, the GUI 1050 may include the displayrepresentation 1052 as well as facilitate user interface with thedisplay representation to input data (such as selecting an area ofinterest on the display representation) via one or more includedinterface features 1054. In this example, a plurality of indications1056 associated with a first part of the type of part are aligned to thedisplay representation of the simulated model 1052. As shown in theinterface features 1054 of FIG. 41B, the indications 1056 correspond to‘Porosity’ indications of a part numbered ‘Serial Number=Unit 100’.FIGS. 42A-B provide an example illustration of the GUI 1050 of FIGS.41A-B, where the display representation of the simulated model of thetype of aircraft part 1052 includes a plurality of aligned indications1058 associated with a second part of the type of part. As shown in theinterface features 1054 of FIG. 42B, the indications 1056 correspond to‘Porosity’ indications of a part numbered ‘Serial Number=Unit 101’. Inthis example, the number of indications increases between the first partand the second part, which may be used to determine a manufacturingtrend for the type of part, and/or determine that the manufacturingprocess for the type of part is not operating properly. In thisparticular example, the increase in the number of indications betweenUnit 100 and Unit 101 indicates that a seam in a mold used in themanufacturing process is likely wearing over time. For example, one ormore seals and/or gaskets may be leaking air into the mold. If similarindications had been previously experienced on parts made in themanufacturing process and manufacturing data associated with a rootcause problem was stored, embodiments of the invention may suggest thesame root cause problem on the GUI by analyzing the aligned indications,manufacturing trend, and/or manufacturing data.

FIG. 43 provides an example control chart 1100 that may be generated bythe computing system 10 and/or user device 12 for a manufacturingprocess based at least in part on NDE data, quality related data,spatially correlated statistics, and/or manufacturing data associatedwith the manufacturing process. In this example, a plurality ofspatially correlated statistics 1102 may be included on the controlchart, where each spatially correlated statistic 1102 corresponds to apart of a type of part manufactured by the manufacturing process. Basedon the spatially correlated statistics 1102, a manufacturing trend 1104may be determined. As discussed, a baseline value and/or an acceptablerange may be defined for a type of part, which in this example isrepresented by dashed line 1106. As shown, many of the spatiallycorrelated statistics 1102 are acceptable based on the acceptable range1106. Moreover, based on the manufacturing trend 1104, embodiments ofthe invention may be able to detect that the manufacturing process willbegin producing unacceptable parts prior to actually manufacturingunacceptable parts (i.e., parts corresponding to the spatiallycorrelated statistics 1102 that are above the acceptable range 1106).For example, the highlighted spatially correlated statistics 1108 mayindicate parts in a sub-rejectable range, and embodiments of theinvention may determine that the manufacturing process is not operatingproperly based at least in part on the trend 1104 and/or the highlightedspatially correlated statistics 1108, such that process adjustments maybe made prior to the manufacture of unacceptable parts.

While the example illustrates a single control chart 1100 for a singlespatially correlated statistic 1102 collected from each part,embodiments of the invention are not so limited. In general, embodimentsof the invention may monitor a manufacturing process by collecting andmonitoring a plurality of spatially correlated statistics for each part,where each spatially correlated statistic for each part may be includedon a corresponding control chart. Therefore, embodiments of theinvention may monitor a plurality of aspects of each part manufacturedby a manufacturing process continuously. In some embodiments suchmonitoring may be substantially in real-time, such that developingand/or potential problems may be addressed in an efficient manner toreduce the production of unacceptable parts.

While the present invention has been illustrated by a description of thevarious embodiments and the examples, and while these embodiments havebeen described in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. Additional advantages and modifications willreadily appear to those skilled in the art. For example, one havingskill in the art will appreciate that multiple filters may be usedwithout departing from the scope of the invention. Moreover, one havingskill in the art will appreciate that a plurality of datasets of NDEdata from a plurality of portions of a plurality of parts may beprocessed without departing from the scope of the invention, and thusembodiments of the invention should not be limited to the modeling,monitoring, and analyzing examples disclosed herein.

Thus, the invention in its broader aspects is therefore not limited tothe specific details, representative apparatus and method, andillustrative example shown and described. Accordingly, departures may bemade from such details without departing from the spirit or scope ofapplicants' general inventive concept.

1-20. (canceled)
 21. A method of modeling the manufacture of a type ofpart with a manufacturing process that includes at least onemanufacturing step in a system including at least one processing unitand at least one memory, the method comprising: receiving nondestructive evaluation (NDE) data associated with the type of part thatcorresponds to data collected during non-destructive evaluation of atleast one particular part of the type of part; aligning the NDE data toat least one corresponding simulated location on a simulated model of atleast a portion of a part of the type of part; and associatingmanufacturing data with the simulated model, wherein the manufacturingdata includes at least one of data indicating a manufacturing step ofthe manufacturing process, data indicating a manufacturing apparatusutilized in the manufacturing process, data indicating a manufacturingtool utilized in the manufacturing process, data indicating a processparameter of the manufacturing process, and data indicating evaluationequipment utilized in collecting the NDE data for a part manufactured bythe manufacturing process; wherein associating the manufacturing datawith the simulated model includes associating the manufacturing datawith at least one corresponding simulated location on the simulatedmodel.
 22. The method of claim 21, further comprising: associating themanufacturing data with NDE data that is aligned to at least oneparticular simulated location.
 23. The method of claim 22 furthercomprising: analyzing NDE data that is aligned to the at least oneparticular simulated location and the associated manufacturing data andidentifying a manufacturing step of the manufacturing process that isassociated with the NDE data aligned to the at least one particularsimulated location.
 24. The method of claim 22 further comprising:analyzing NDE data that is aligned to the at least one particularsimulated location and the associated manufacturing data and identifyinga manufacturing apparatus that is utilized in the manufacturing processassociated with the NDE data aligned to the at least one particularsimulated location.
 25. The method of claim 22 further comprising:analyzing NDE data that is aligned to that at least one particularsimulated location and the associated manufacturing data and identifyinga manufacturing tool that is utilized in the manufacturing processassociated with the NDE data aligned to the at least one particularsimulated location.
 26. The method of claim 21, wherein themanufacturing data includes data indicating evaluation equipment that isutilized in collecting the NDE data during non-destructive evaluation ofthe at least one particular part, the method further comprising:analyzing the NDE data and the aligned NDE data to determine whether theevaluation equipment utilized in collecting the NDE data is operatingproperly.
 27. A method of analyzing a manufactured part of a particulartype of part, wherein the manufactured part is manufactured using amanufacturing process that includes a plurality of manufacturing stepsin a system including at least one processing unit and at least onememory, the method comprising: aligning a non-destructive evaluation(NDE) dataset associated with the manufactured part to a simulated modelof at least of a portion of a part of the particular type of part,including aligning NDE data associated with an area of interest on themanufactured part to at least one corresponding simulated location onthe simulated model; the NDE dataset corresponding to data collectedduring non-destructive evaluation of the associated manufactured part;and analyzing the aligned NDE data associated with the area of intereston the manufactured part and the at least one corresponding simulatedlocation to determine whether the manufactured part includes amanufacturing defect that is associated with the area of interest. 28.The method of claim 27, wherein analyzing the aligned NDE dataassociated with the area of interest on the manufactured part and the atleast one corresponding simulated location includes determining aspatially correlated statistic for the area of interest for themanufactured part, and comparing the spatially correlated statistic tobaseline data associated with the area of interest on the simulatedmodel.
 29. The method of claim 27 further comprising: in response todetermining that the manufactured part includes a manufacturing defectassociated with the area of interest, aligning an indication of themanufacturing defect to at least one corresponding simulated location onthe simulated model.
 30. The method of claim 29, further comprising: inresponse to determining that the manufactured part includes amanufacturing defect associated with the area of interest, analyzing thealigned manufacturing defect to determine a root cause problemassociated with the at least one corresponding simulated location of thealigned manufacturing defect on the simulated model.
 31. The method ofclaim 30, wherein analyzing the aligned manufacturing defect includesanalyzing manufacturing data that is associated with the at least onecorresponding simulated location of the aligned manufacturing defect onthe simulated model; and the associated manufacturing data of the atleast one corresponding simulated location of the aligned manufacturingdefect including historical data for the type of part indicating anymanufacturing defects associated with least one corresponding simulatedlocation of the aligned manufacturing defect and any root cause problemsthat are associated with the manufacturing defects for the type of part.32. The method of claim 29, further comprising: in response todetermining that the manufactured part includes a manufacturing defectassociated with the area of interest, analyzing the aligned NDE dataassociated with the area of interest, the at least one correspondingsimulated location of the aligned manufacturing defect on the simulatedmodel, and the aligned manufacturing defect and determining amanufacturing step that is associated with the manufacturing defect onthe manufactured part.
 33. The method of claim 32, further comprising:analyzing the aligned NDE data, the at least one corresponding simulatedlocation and the identified manufacturing defect to identify at leastone manufacturing apparatus which is utilized in the at least onemanufacturing step that is associated with the identified manufacturingdefect on the manufactured part.
 34. The method of claim 33, furthercomprising: analyzing the aligned NDE data, the at least onecorresponding simulated location, and the identified manufacturingdefect to identify at least one manufacturing tool which is associatedwith the manufacturing apparatus that is associated with themanufacturing defect on the manufactured part.
 35. A method ofmonitoring the manufacture of a composite aircraft part of a particulartype of composite aircraft part by a manufacturing process that has asystem including at least one processing unit and at least one memory,the method comprising: receiving a plurality of non-destructiveevaluation (NDE) datasets, wherein each NDE dataset is associated withat least a portion of a manufactured composite aircraft part of theparticular type; each NDE dataset corresponding to data collected duringnon-destructive evaluation of the associated at least a portion of themanufactured composite aircraft part; aligning the plurality of NDEdatasets to a simulated model associated with the at least a portion ofthe particular type of composite aircraft part; and analyzing eachaligned NDE dataset to determine at least one spatially correlatedstatistic for each composite aircraft part.
 36. The method of claim 35further comprising: aligning each spatially correlated statistic to atleast one corresponding simulated location on the simulated model. 37.The method of claim 36 further comprising: analyzing the alignedspatially correlated statistics for at least a subset of the compositeaircraft parts to determine a manufacturing trend associated with themanufacturing process based on the analyzed spatially correlatedstatistics.
 38. The method of claim 37 further comprising: analyzing themanufacturing trend and base line data that is associated with the atleast one corresponding simulated location on the simulated model todetermine whether the manufacturing process is operating properly. 39.The method of claim 38, further comprising: in response to determiningthat the manufacturing process is not operating properly, determining aroot cause problem associated with the manufacturing process based atleast in part on the aligned NDE datasets.
 40. The method of claim 35,wherein the spatially correlated statistic is one of average porosity,average thickness or average distance for a region of the at least aportion of a manufactured composite aircraft part of the particulartype.
 41. The method of claim 35, further comprising: receivingmanufacturing data associated with the manufacturing process for theparticular type of part; the manufacturing data including at least oneof: data indicating a manufacturing step of the manufacturing processassociated with at least one physical location on the particular type ofpart, data indicating a manufacturing apparatus utilized in themanufacturing process associated with at least one physical location onthe particular type of part, data indicating a manufacturing parameterof the manufacturing process associated with at least one physicallocation on the particular type of part, data indicating a manufacturingtool utilized in the manufacturing process associated with at least onephysical location on the particular type of part, and data indicating atleast one possible root cause problem of the manufacturing processassociated with at least one physical location on the particular type ofpart; and associating the manufacturing data with the simulated model.42. A method of monitoring the manufacture of a particular type of partby a manufacturing process including a plurality of manufacturing stepsin a system including at least one processing unit and a memory, themethod comprising: receiving non-compliance reports corresponding to themanufacture of parts of the type of part, with at least onenon-compliance report indicating at least one visually detected defectcorresponding to a particular location on a particular part; aligningeach visually detected defect to at least one simulated location of asimulated model associated with at least a portion of a part of the typeof part, wherein the at least one simulated location corresponds to theparticular location corresponding to the visually detected defect; andanalyzing the aligned visually detected defects to determine whether amanufacturing problem is occurring in the manufacturing process.